Interpretive Summary: Fusarium head blight, also known as scab, is a fungal disease that occurs in small grains. In the United States it is most problematic in wheat (hard red spring, durum, and soft red winter classes) and barley. Scab may produce the mycotoxin deoxynivalenol (DON), also known as vomitoxin, which is toxic to non-ruminant animals. U.S. Food and Drug Administration advisory levels specify that DON in finished wheat products destined for human consumption should not exceed 1 part per million. Traditionally, official inspection for scab entails human visual analysis of sample of hundreds of kernels, thus requiring 10 minutes or longer per sample. In the current study, a version of digital image analysis known as hyperspectral imaging was used to explore the feasibility of an objectively based, automated inspection system for wheat scab. Images at discrete wavelengths in the visible and short wavelength near-infrared regions were collected on multi-kernel samples of scab-damaged and non-damaged wheat. Statistical analysis of the images demonstrated that classification into scab and non-scab classes is possible using as few as two wavelengths. Pending additional research, it appears feasible to develop a two-to-four wavelength imaging system for use in official grain inspection, as well as in the grain trade and processing industries.

Technical Abstract:
Scab (Fusarium head blight) is a disease that causes wheat kernels to be shriveled, underweight, and difficult to mill. Scab is also a health concern because of the possible concomitant production of the mycotoxin deoxynivalenol. Current official inspection procedures entail manual human inspection. A study was undertaken to explore the possibility of detecting gscab-damaged wheat kernels by machine vision. A custom-made hyperspectral imaging system, possessing a wavelength range of 425 to 860 nm with neighboring bands 3.7 nm apart, a spatial resolution of 0.022 mm**2/pixel, and 16-bit per pixel dynamic range, gathered images of non-touching kernels from three wheat varieties. Each variety was represented by 32 normal and 32 scab-damaged kernels. From a search of wavelengths that could be used to separate the two classes (normal vs. scab), a linear discriminant function was constructed from the best R(lambda1)/R(lambda2), based on the assumption of a multivariate normal distribution for each class and the pooling of the covariance matrix across the two classes. Results showed that the ratio R(568 nm)/R(715 nm) provided a cross-validation misclassification error that averaged between 2 and 17%, dependent on wheat variety. With expansion to the testing of more varieties, a two-to-four wavelength machine vision system appears to be a feasible alternative to manual inspection.